フルテキストファイル
著者
Kotaro, Mukai Graduate School of Sustainability Science Tottori University
Nakanishi, Isao Faculty of Engineering, Tottori University 研究者総覧 KAKEN
キーワード
Biometric authentication
Electroencephalogram(EEG)
Ultrasound
Fractal dimension
Calculation amount
抄録
The aim of this study is to authenticate individuals using an electroencephalogram (EEG) evoked by a stimulus. EEGs are highly confidential and enable continuous authentication during the use of or access to the given information or service. However, perceivable stimulation distracts the users from the activity they are carrying out while using the service. Therefore, ultrasound stimuli were chosen for EEG evocation. In our previous study, an Equal Error Rate (EER) of 0 % was achieved; however, there were some features which had not been evaluated. In this paper, we introduce a new type of feature, namely fractal dimension, as a nonlinear feature, and evaluate its verification performance on its own and in combination with other conventional features. As a result, an EER of 0 % was achieved when using five features and 14 electrodes, which accounted for 70 support vector machine (SVM) models. However, the construction of the 70 SVM models required extensive calculations. Thus, we reduced the number of SVM models to 24 while maintaining an EER = 0 %.
資料タイプ
会議資料
掲載誌名
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
最新掲載誌名
Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020)
発行日
2020-11
著者版フラグ
著者版
著作権表記
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
掲載情報
K. Mukai, I Nakanishi. Introduction of Fractal Dimension Feature and Reduction of Calculation Amount in Person Authentication Using Evoked EEG by Ultrasound. Proceedings of the 2020 IEEE Region 10 Conference (TENCON2020). 2020
部局名
工学部・工学研究科
言語
英語